Acuna lab is looking for students to optimize science using machine learning

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About the lab

Dr. Acuna is an Assistant Professor in the School of Information Studies
at Syracuse University. He currently
works on mathematical and computational models of scientific discovery, predictability,
and integrity. Please take a moment to look
at his background, research, and recent grants.

Professor Acuna teaches courses for the
Applied Data Science
and Information Management graduate degrees. He is currently the teacher and Professor of Record for
the course IST 718: Advanced Information Analytics.

Past Master’s students have done internships in Silicon Valley (e.g., Airbnb, Google),
are working in major consulting companies (e.g., Ernst & Young, Goldman Sachs), and are
broadly working as data scientists. Please see the People section.

About the position

Professor Acuna is looking for Ph.D. and first-year Master’s students to work on an exciting new project
around the mathematical and computational modeling of scientific peer review. The ideal candidate
should have an undergraduate major in Computer Science, Engineering, Applied Statistics, Mathematics,
or a similar quantitative field. The work to be done revolves around a recently-awarded
NSF grant.

Requirements

Develop reproducible software and tools to optimally match reviewers and manuscripts based on
mathematical objective functions

Write method and result sections for scientific manuscripts

Have advanced computer programming skills in languages such as Python and R. SQL is also
desirable

Apply

Your Github repository, preferably with code from a personal project rather than a “class project”.

Your transcripts

Your GRE, GMAT, or equivalent scores

If you have any questions, do not hesitate in contacting me.
If you are thinking of applying to the Ph.D. program, we have a very competitive fully-funded program, and you
should contact me first. Otherwise,
apply to the Ph.D. program
and mention my name in you materials.

Part of the funding for these positions has been generously provided by the National Science Foundation awards
#1646763 and #1800956